{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.647862Z",
     "start_time": "2023-04-22T12:20:54.968535Z"
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif'] = ['Microsoft YaHei']"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 准备数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.663373Z",
     "start_time": "2023-04-22T12:20:55.648863Z"
    }
   },
   "outputs": [],
   "source": [
    "x0 = np.linspace(-2, 2, 1000)\n",
    "y0 = 2 * np.sin(x0)\n",
    "y0 += 0.32 * np.random.randn(y0.shape[0]) # 加噪声"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "给x和y加一维"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.679372Z",
     "start_time": "2023-04-22T12:20:55.666373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = x0[:, np.newaxis]\n",
    "y = y0[:, np.newaxis]\n",
    "x.shape"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 线性回归损失作为基准（baseline）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.695404Z",
     "start_time": "2023-04-22T12:20:55.681373Z"
    }
   },
   "outputs": [],
   "source": [
    "def mse(yl, y0):\n",
    "    return ((yl - y0)**2).sum()/yl.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.711371Z",
     "start_time": "2023-04-22T12:20:55.696372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "回归方程为: y = 0.00 + 1.31 * x\n"
     ]
    }
   ],
   "source": [
    "b2 = float((len(y0)*(x0*y0).sum(0) - y.sum(0)*x0.sum(0)) /\n",
    "           (len(x0)*(x0**2).sum(0) - (x.sum(0)**2)))\n",
    "b1 = float(y0.mean() - b2 * x0.mean())\n",
    "print('回归方程为: y = {:.2f} + {:.2f} * x'.format(b1, b2))\n",
    "# 数据是标准化的，因此截距项为0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.727390Z",
     "start_time": "2023-04-22T12:20:55.713370Z"
    }
   },
   "outputs": [],
   "source": [
    "yl = []\n",
    "for i in x0:\n",
    "    yl.append(b1 + b2 * i)\n",
    "yl = np.array(yl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.743379Z",
     "start_time": "2023-04-22T12:20:55.729372Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "线性回归平均损失:0.2212\n"
     ]
    }
   ],
   "source": [
    "baseline = mse(yl, y0) # type: ignore\n",
    "print('线性回归平均损失:{:.4f}'.format(baseline))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:55.918431Z",
     "start_time": "2023-04-22T12:20:55.744380Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c48015f580>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x0, y0, label='原始点')\n",
    "plt.plot(x, yl, 'r', label='最小二乘法回归线, 损失{:.3f}'.format(baseline))\n",
    "plt.legend(fontsize='13')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tensorflow/keras神经网络框架"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:58.342215Z",
     "start_time": "2023-04-22T12:20:55.920430Z"
    }
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:59.114910Z",
     "start_time": "2023-04-22T12:20:58.343198Z"
    }
   },
   "outputs": [],
   "source": [
    "# type: ignore\n",
    "model_tf = keras.models.Sequential([ \n",
    "    keras.layers.Dense(5, input_shape=[1,], activation='sigmoid'),\n",
    "    keras.layers.Dense(1),\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:59.130787Z",
     "start_time": "2023-04-22T12:20:59.115892Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " dense (Dense)               (None, 5)                 10        \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 6         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 16\n",
      "Trainable params: 16\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model_tf.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:20:59.146831Z",
     "start_time": "2023-04-22T12:20:59.133787Z"
    }
   },
   "outputs": [],
   "source": [
    "model_tf.compile(loss='mse', optimizer=keras.optimizers.SGD(0.01))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:05.578232Z",
     "start_time": "2023-04-22T12:20:59.147814Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "22/22 [==============================] - 3s 12ms/step - loss: 2.2725 - val_loss: 5.4026\n",
      "Epoch 2/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 1.4517 - val_loss: 6.4075\n",
      "Epoch 3/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 1.3147 - val_loss: 6.3813\n",
      "Epoch 4/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 1.2500 - val_loss: 6.2519\n",
      "Epoch 5/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 1.1908 - val_loss: 5.9744\n",
      "Epoch 6/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 1.1338 - val_loss: 5.7057\n",
      "Epoch 7/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 1.0769 - val_loss: 5.3857\n",
      "Epoch 8/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 1.0191 - val_loss: 5.0499\n",
      "Epoch 9/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.9603 - val_loss: 4.6759\n",
      "Epoch 10/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.9011 - val_loss: 4.3626\n",
      "Epoch 11/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.8420 - val_loss: 3.9890\n",
      "Epoch 12/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.7833 - val_loss: 3.6886\n",
      "Epoch 13/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.7248 - val_loss: 3.3731\n",
      "Epoch 14/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.6685 - val_loss: 3.0462\n",
      "Epoch 15/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.6142 - val_loss: 2.7437\n",
      "Epoch 16/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.5615 - val_loss: 2.4672\n",
      "Epoch 17/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.5119 - val_loss: 2.1954\n",
      "Epoch 18/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.4664 - val_loss: 1.9608\n",
      "Epoch 19/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.4234 - val_loss: 1.7227\n",
      "Epoch 20/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.3847 - val_loss: 1.5306\n",
      "Epoch 21/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.3496 - val_loss: 1.3456\n",
      "Epoch 22/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.3184 - val_loss: 1.1831\n",
      "Epoch 23/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.2909 - val_loss: 1.0549\n",
      "Epoch 24/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.2666 - val_loss: 0.9286\n",
      "Epoch 25/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.2456 - val_loss: 0.8073\n",
      "Epoch 26/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.2275 - val_loss: 0.7132\n",
      "Epoch 27/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.2118 - val_loss: 0.6360\n",
      "Epoch 28/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.1984 - val_loss: 0.5624\n",
      "Epoch 29/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1872 - val_loss: 0.5002\n",
      "Epoch 30/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.1778 - val_loss: 0.4514\n",
      "Epoch 31/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.1697 - val_loss: 0.4066\n",
      "Epoch 32/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.1632 - val_loss: 0.3662\n",
      "Epoch 33/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.1576 - val_loss: 0.3376\n",
      "Epoch 34/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.1529 - val_loss: 0.3077\n",
      "Epoch 35/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.1489 - val_loss: 0.2897\n",
      "Epoch 36/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1457 - val_loss: 0.2679\n",
      "Epoch 37/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1431 - val_loss: 0.2502\n",
      "Epoch 38/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1410 - val_loss: 0.2392\n",
      "Epoch 39/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1390 - val_loss: 0.2274\n",
      "Epoch 40/50\n",
      "22/22 [==============================] - 0s 8ms/step - loss: 0.1374 - val_loss: 0.2181\n",
      "Epoch 41/50\n",
      "22/22 [==============================] - 0s 9ms/step - loss: 0.1360 - val_loss: 0.2102\n",
      "Epoch 42/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1349 - val_loss: 0.2031\n",
      "Epoch 43/50\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.1338 - val_loss: 0.1992\n",
      "Epoch 44/50\n",
      "22/22 [==============================] - 0s 6ms/step - loss: 0.1331 - val_loss: 0.1916\n",
      "Epoch 45/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1321 - val_loss: 0.1873\n",
      "Epoch 46/50\n",
      "22/22 [==============================] - 0s 11ms/step - loss: 0.1315 - val_loss: 0.1814\n",
      "Epoch 47/50\n",
      "22/22 [==============================] - 0s 10ms/step - loss: 0.1311 - val_loss: 0.1789\n",
      "Epoch 48/50\n",
      "22/22 [==============================] - 0s 7ms/step - loss: 0.1303 - val_loss: 0.1751\n",
      "Epoch 49/50\n",
      "22/22 [==============================] - 0s 13ms/step - loss: 0.1299 - val_loss: 0.1725\n",
      "Epoch 50/50\n",
      "22/22 [==============================] - 0s 14ms/step - loss: 0.1293 - val_loss: 0.1704\n"
     ]
    }
   ],
   "source": [
    "history = model_tf.fit(x, y, epochs=50, validation_split=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:05.783017Z",
     "start_time": "2023-04-22T12:21:05.579233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "32/32 [==============================] - 0s 3ms/step\n"
     ]
    }
   ],
   "source": [
    "y_tf = model_tf.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:05.798990Z",
     "start_time": "2023-04-22T12:21:05.784992Z"
    }
   },
   "outputs": [],
   "source": [
    "keras_mse = mse(y_tf, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.003129Z",
     "start_time": "2023-04-22T12:21:05.800989Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.DataFrame(history.history).plot(figsize=(8, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.284975Z",
     "start_time": "2023-04-22T12:21:06.006127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2c7abf9d390>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x0, y0, c='gray', label='原始点')\n",
    "plt.plot(x0, yl, 'r', label='最小二乘法回归线, MSE为 {:.3f}'.format(baseline))\n",
    "plt.plot(x0, y_tf, 'yellow', label='keras神经网络, MSE为 {:.3f}'.format(keras_mse))\n",
    "plt.legend(fontsize='12')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 手搓神经网络框架"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.315964Z",
     "start_time": "2023-04-22T12:21:06.285947Z"
    }
   },
   "outputs": [],
   "source": [
    "class BP():\n",
    "    def __init__(self) -> None:\n",
    "        pass\n",
    "\n",
    "    def activate(self, t):\n",
    "        '''\n",
    "        激活函数， sigmoid 或 relu\n",
    "        '''\n",
    "        if self.activator == 'sigmoid':\n",
    "            return 1/(1+np.e**(-t))\n",
    "        if self.activator == 'relu':\n",
    "            return np.maximum(t, 0)\n",
    "\n",
    "    def input_forward_hidden(self, x):\n",
    "        ''' \n",
    "        输入层到隐藏层\n",
    "        y11 = w1x1 + b1 \\\\\n",
    "        输出 y12, 大小 (5, 1) -> (神经元数量, x数量) (y11在反向传播用不到)\n",
    "        '''\n",
    "        x = x[:, np.newaxis]\n",
    "        y11 = self.w1 @ x + self.b1 # 叉乘**\n",
    "        y12 = self.activate(y11)\n",
    "        return y12\n",
    "\n",
    "    def hidden_forward_output(self, y12):\n",
    "        ''' \n",
    "        隐藏层到输出层, 这里无需激活函数\n",
    "        y21 = y12 * w2 + b2 \\\\\n",
    "        网络最终输出 y21, 大小 (1, 1) -> (神经元数量, y数量)\n",
    "        '''\n",
    "        y21 = self.w2 @ y12 + self.b2 # 叉乘**\n",
    "        return y21\n",
    "\n",
    "    def metrics(self, y_p, y_t, loss='mse') -> float: # type: ignore\n",
    "        '''\n",
    "        计算误差\n",
    "        '''\n",
    "        if loss == 'mse':  # 均方差\n",
    "            return (((y_p - y_t)**2).sum()/2)\n",
    "\n",
    "    # 下面是四个参数的反向传播更新，公式是手推链式求导直接写的**\n",
    "    def update_w2(self, yt, y21, y12, lr=0.02):\n",
    "        '''\n",
    "        更新输出层权重,先是输出层w2\n",
    "        '''\n",
    "        delta_w2 = (y21 - yt).sum() * y12.T\n",
    "        return self.w2 - lr * delta_w2\n",
    "\n",
    "    def update_b2(self, yt, y21, lr=0.02):\n",
    "        delta_b2 = (y21 - yt).sum()\n",
    "        return self.b2 - lr * delta_b2\n",
    "    \n",
    "    def update_w1(self, yt, y21, y12, x, lr=0.02):\n",
    "        delta_w1 = (y21 - yt).sum() * self.w2.T * y12 * (1 - y12) * x.T\n",
    "        return self.w1 - lr * delta_w1\n",
    "    \n",
    "    def update_b1(self, yt, y21, y12, lr=0.02):\n",
    "        delta_b1 = (y21 - yt).sum() * self.w2.T * y12 * (1 - y12)\n",
    "        return self.b1 - lr * delta_b1\n",
    "    \n",
    "    def fit(self, x, y, activator):\n",
    "        self.w1 = np.random.randn(20, 1) \n",
    "        # 👆大小规则：本层神经元数量, 一次输入一个，可以用batch一次多个**\n",
    "        self.b1 = np.random.randn(5, 1) \n",
    "        # 👆大小规则：本层神经元数量, 一维神经网络就一层**\n",
    "        self.w2 = np.random.randn(1, self.w1.shape[0]) \n",
    "        # 👆大小规则：本层神经元数量，上一层神经元数量**\n",
    "        self.b2 = np.random.randn(1, 1) \n",
    "        # 👆大小规则：本层神经元数量, 一维神经网络就一层**\n",
    "        self.y = y\n",
    "        self.x = x\n",
    "        self.mse = 0\n",
    "        self.activator = activator\n",
    "        \n",
    "        # 统计损失\n",
    "        self.list_mse = []\n",
    "        \n",
    "    def train(self, epochs:int, lr=0.02, loss_type='mse', target_error=0.0001):\n",
    "        for _ in range(epochs): \n",
    "            for xs, yt in zip(self.x, self.y): # x_sample, y_true(label)\n",
    "                y12 = self.input_forward_hidden(xs)\n",
    "                y21 = self.hidden_forward_output(y12)\n",
    "                self.w2 = self.update_w2(yt, y21, y12, lr)\n",
    "                self.b2 = self.update_b2(yt, y21, lr)\n",
    "                self.w1 = self.update_w1(yt, y21, y12, xs, lr)\n",
    "                self.b1 = self.update_b1(yt, y21, y12, lr)\n",
    "                self.mse = self.metrics(y21, yt, loss_type) # type: ignore\n",
    "            self.list_mse.append(self.mse)\n",
    "                \n",
    "            if self.mse <= target_error:\n",
    "                print('✅ 共{:d}轮, 达到目标损失, \\\n",
    "                    最终损失E={:.5f}'.format(_+1, self.mse))\n",
    "                break\n",
    "            \n",
    "            if _ > 15 and np.mean(self.list_mse[-20: -1]) < self.list_mse[-1]:\n",
    "                print('✅ 共{:d}轮, 损失反向增大，自动停止, \\\n",
    "                      最终损失E={:.5f}'.format(_+1, self.mse))\n",
    "                break\n",
    "            \n",
    "            elif _ == epochs - 1 and self.mse > target_error:\n",
    "                print('❌ 共{:d}轮, 最终损失E={:.5f}, \\\n",
    "                    未达到目标损失 {:.5f}'.format(_+1,self.mse, target_error))\n",
    "                break\n",
    "            \n",
    "    def fit_transform(self, x, y, epochs:int, lr=0.01, activator='sigmoid', \n",
    "                      loss_type='mse', target_error=0.00):\n",
    "        '''\n",
    "        epochs: 最大轮次, 每一轮次过一次全部数据 \\\\\n",
    "        lr: 学习率, 默认0.01 \\\\\n",
    "        activator: 'sigmoid' 或 'relu'(尚未配适反向传播, 勿用) \\\\\n",
    "        loss_type: 损失类型, 目前仅有 \"mse\"\n",
    "        '''\n",
    "        self.activator = activator\n",
    "        self.fit(x, y, self.activator)\n",
    "        self.train(epochs, lr, loss_type, target_error)\n",
    "        \n",
    "    def predict(self, new_x):\n",
    "        y_lst = []\n",
    "        for i in new_x:\n",
    "            y0 = self.input_forward_hidden(i)\n",
    "            y1 = self.hidden_forward_output(y0)\n",
    "            y_lst.append(y1)\n",
    "        y_lst = np.array(y_lst)\n",
    "        y_lst = y_lst.reshape(y_lst.shape[0])\n",
    "        return y_lst\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:06.331944Z",
     "start_time": "2023-04-22T12:21:06.317964Z"
    }
   },
   "outputs": [],
   "source": [
    "model = BP()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:07.797004Z",
     "start_time": "2023-04-22T12:21:06.333947Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "✅ 共28轮, 损失反向增大，自动停止, 最终损失E=0.00238\n"
     ]
    }
   ],
   "source": [
    "model.fit_transform(x, y, 100, lr=0.0005, activator='sigmoid')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 损失情况"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.033117Z",
     "start_time": "2023-04-22T12:21:07.800005Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1d32c2641c0>]"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.title('损失情况', fontsize=15)\n",
    "plt.ylabel('损失', fontsize=15)\n",
    "plt.xlabel('训练回合', fontsize=15)\n",
    "plt.grid()\n",
    "plt.plot(model.list_mse, label='神经网络')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.049114Z",
     "start_time": "2023-04-22T12:21:08.034115Z"
    }
   },
   "outputs": [],
   "source": [
    "yp = model.predict(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.064119Z",
     "start_time": "2023-04-22T12:21:08.050114Z"
    }
   },
   "outputs": [],
   "source": [
    "bp_mse = mse(yp, y0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2023-04-22T12:21:08.365294Z",
     "start_time": "2023-04-22T12:21:08.065620Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1d32dc8f0d0>"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.text(0, -2.5, 'y=2sinx', fontsize=15)\n",
    "plt.scatter(x, y, s=3, c='gray', label='原散点')\n",
    "plt.plot(x0, yl, 'r', label='最小二乘法回归线, mse = {:.3f}'.format(baseline))\n",
    "plt.plot(x0, y_tf, 'green', label='keras神经网络, mse = {:.3f}'.format(keras_mse))\n",
    "plt.plot(x, yp, c='b', label='手搓神经网络, mse = {:.3f}'.format(bp_mse))\n",
    "plt.legend(fontsize=10)"
   ]
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